What is the input to AlphaGo's neural network?
The input to the neural network is a $19 × 19 × 17$ image stack
  comprising $17$ binary feature planes. $8$ feature planes $X_t$ consist of binary values indicating the
  presence of the current player’s stones ($X^i_t = 1$ if intersection $i$ contains a stone of the player’s
  colour at time-step $t$; $0$ if the intersection is empty, contains an opponent stone, or if $t &lt; 0$). A
  further $8$ feature planes, $Y_t$
  , represent the corresponding features for the opponent’s stones. The
  final feature plane, $C$, represents the colour to play, and has a constant value of either $1$ if black
  is to play or $0$ if white is to play. These planes are concatenated together to give input features
  $s_t = [
X_t, Y_t, X_{t−1}, Y_{t−1}, ..., X_{t−7}, Y_{t−7}, C]$.


This and all the other architecture details can be found in the "Neural Network Architecture" section in the paper.